Análise do desempenho operacional do sistema de transporte público de passageiros a partir de dados massivos

• Ano de defesa: 2023
• Autor(a) principal: Sousa Junior, José Nauri Cazuza de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Transporte público; Análise Envoltória de Dados; Desempenho; CNPQ::ENGENHARIAS::ENGENHARIA DE TRANSPORTES
• Link de acesso: http://repositorio.ufc.br/handle/riufc/75505

Public transportation operation represents a complex system involving various stakeholders and influenced by several activities, rendering monitoring, analysis, and evaluation challenging tasks. Traditionally, operation assessment relies on sample surveys or simplified data, leading to significant uncertainties. In recent years, the adoption of Intelligent Transportation Systems (ITS) has enabled the comprehensive collection of public transportation supply and demand data through fare and passenger control systems, GPS tracking, and General Transit Feed Specification (GTFS). This, in turn, has facilitated the creation of databases for diverse purposes, including travel pattern analysis, user behavior profiling, destination estimation, Origin-Destination matrix construction, performance analysis, system planning, and numerous other studies. This thesis presents a methodology for analyzing the performance of public transportation operations using massive data sets. The research aims to contribute to the enhancement of public transportation service performance. Data from the public transportation system in the Metropolitan Region of Fortaleza were utilized to assess the proposed methodology’s applicability in public transportation services. This data allowed for the characterization of operational aspects in both time and space domains and facilitated performance analysis, focusing on identifying areas where operation efficiency can be improved. The Public Transportation System can be evaluated from various perspectives (passenger, operator, society, government) and through multiple factors (internal and external). A four-step Data Envelopment Analysis (DEA) method was applied, evaluating service efficiency by routes and route groups, incorporating external factors, and assessing route productivity evolution over the years (2016 to 2022). The primary methodological contribution of this thesis lies in the utilization of massive data available in various public transportation systems as a decision-support tool for public transportation authorities and operators. This approach enables evaluating efficiency and identifying routes that can enhance operational performance, ultimately leading to a more efficient public transportation system.